STATS 402 Video Project
Hengyuan Liu
2023-10-26
library(corrplot)## corrplot 0.92 loadedlibrary(ggplot2)
library(reshape2)
library(liver)##
## Attaching package: 'liver'## The following object is masked from 'package:base':
##
## transformlibrary(tidyr)##
## Attaching package: 'tidyr'## The following object is masked from 'package:reshape2':
##
## smithslibrary(leaps)
library(GGally)## Registered S3 method overwritten by 'GGally':
## method from
## +.gg ggplot2data <- data(house)length(which(is.na(house), arr.ind=TRUE))## [1] 0EDA
summary(house)## house.age distance.to.MRT stores.number latitude
## Min. : 0.000 Min. : 23.38 Min. : 0.000 Min. :24.93
## 1st Qu.: 9.025 1st Qu.: 289.32 1st Qu.: 1.000 1st Qu.:24.96
## Median :16.100 Median : 492.23 Median : 4.000 Median :24.97
## Mean :17.713 Mean :1083.89 Mean : 4.094 Mean :24.97
## 3rd Qu.:28.150 3rd Qu.:1454.28 3rd Qu.: 6.000 3rd Qu.:24.98
## Max. :43.800 Max. :6488.02 Max. :10.000 Max. :25.01
## longitude unit.price
## Min. :121.5 Min. : 7.60
## 1st Qu.:121.5 1st Qu.: 27.70
## Median :121.5 Median : 38.45
## Mean :121.5 Mean : 37.98
## 3rd Qu.:121.5 3rd Qu.: 46.60
## Max. :121.6 Max. :117.50# Example data frame structure
library(leaflet)
# Create a leaflet map
map <- leaflet(data = house) %>%
addTiles() # You can choose different tilesets with addProviderTiles() if desired
# Add markers to the map based on latitude and longitude
map <- map %>% addMarkers(
lat = ~latitude,
lng = ~longitude,
label = ~unit.price,
popup = ~paste("Median House Price: $", unit.price),
clusterOptions = markerClusterOptions()
)
# Display the map
mapboxplot(house$house.age)
boxplot(house$distance.to.MRT)
boxplot(house$stores.number)
boxplot(house$latitude)
boxplot(house$longitude)
boxplot(house$unit.price, main="Unit Price")
library(ggplot2)
# Create a list of plots
plots <- list()
# Loop through each numeric variable and create a density plot with a line
for (i in 1:ncol(house)) {
p <- ggplot(house, aes(x = house[, i])) +
geom_density(color = "blue") +
labs(title = colnames(house)[i], x = "Value", y = "Density")
plots[[i]] <- p
}
# Print the plots one by one
for (i in 1:ncol(house)) {
print(plots[[i]])
}
correlation_matrix <- cor(house)
corrplot(correlation_matrix)
hist(house$unit.price, main="Distribution of Unit Price", xlab="Unit Price")
library(car)## Loading required package: carDatasymbox(house$unit.price, ylab = "unit price", main = "Boxplots for Each Transformations of Unit Price")
transprice <- (house$unit.price)^0.5
hist(transprice, ylab = "unit price", main="Distribution of Unit Price in Square root Transformation", xlab="Unit Price")
names(house)## [1] "house.age" "distance.to.MRT" "stores.number" "latitude"
## [5] "longitude" "unit.price"EDA: Check Assumption
pairs(house)
ggpairs( house )
Model:
\[ Y_{unit.price} = -580.5 -0.02*X_{house.age} -0.0004*X_{distance.to.MRT} + 0.09*X_{stores.number} + 21.83*X_{latitude}+0.365*X_{longitude}\]
model1 <- lm(unit.price^0.5 ~., data=house)
summary(model1)##
## Call:
## lm(formula = unit.price^0.5 ~ ., data = house)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.6426 -0.3926 -0.0687 0.3552 4.4799
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -5.805e+02 4.706e+02 -1.233 0.218
## house.age -2.132e-02 2.955e-03 -7.216 2.63e-12 ***
## distance.to.MRT -3.782e-04 5.481e-05 -6.900 1.99e-11 ***
## stores.number 9.132e-02 1.441e-02 6.336 6.25e-10 ***
## latitude 2.183e+01 3.406e+00 6.409 4.05e-10 ***
## longitude 3.446e-01 3.724e+00 0.093 0.926
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6793 on 408 degrees of freedom
## Multiple R-squared: 0.6381, Adjusted R-squared: 0.6337
## F-statistic: 143.9 on 5 and 408 DF, p-value: < 2.2e-16plot(model1)
s = summary(model1)
data.frame(s$coefficients)## Estimate Std..Error t.value Pr...t..
## (Intercept) -5.804553e+02 4.706491e+02 -1.23330794 2.181710e-01
## house.age -2.132262e-02 2.954890e-03 -7.21604710 2.629399e-12
## distance.to.MRT -3.782137e-04 5.481049e-05 -6.90038863 1.989437e-11
## stores.number 9.132277e-02 1.441392e-02 6.33573470 6.252533e-10
## latitude 2.182880e+01 3.405926e+00 6.40906402 4.047266e-10
## longitude 3.446381e-01 3.724248e+00 0.09253897 9.263153e-01s$r.squared## [1] 0.6381435Remove Outliers
#Outliers location
outliers <- rstandard(model1)[rstandard(model1) < -2 | rstandard(model1) > 2] #leverage plot
matrix <- as.matrix(outliers)
rownames <- rownames(matrix)
levoutlier<-as.numeric(rownames)
length(levoutlier)## [1] 20outliersmore <- which(model1$fitted.values < 4.5) #residual plot
mat <- as.matrix(outliersmore)
row <- rownames(mat)
resoutlier<-as.numeric(row)
length(resoutlier)## [1] 36outliers <- union(levoutlier, resoutlier)length(outliers)## [1] 53#New data set without the outliers
data_no_outlier <- house[-outliers,]model2 <- lm(unit.price^0.5 ~., data=data_no_outlier)
summary(model2)##
## Call:
## lm(formula = unit.price^0.5 ~ ., data = data_no_outlier)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.25287 -0.28715 0.00386 0.28657 1.66668
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.159e+03 3.240e+02 -3.576 0.000397 ***
## house.age -2.896e-02 2.101e-03 -13.781 < 2e-16 ***
## distance.to.MRT -5.908e-04 4.583e-05 -12.891 < 2e-16 ***
## stores.number 6.768e-02 1.035e-02 6.539 2.16e-10 ***
## latitude 2.966e+01 2.496e+00 11.884 < 2e-16 ***
## longitude 3.496e+00 2.547e+00 1.373 0.170742
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.455 on 355 degrees of freedom
## Multiple R-squared: 0.7455, Adjusted R-squared: 0.7419
## F-statistic: 208 on 5 and 355 DF, p-value: < 2.2e-16plot(model2)
s2 <- summary(model2)
data.frame(s2$coefficients)## Estimate Std..Error t.value Pr...t..
## (Intercept) -1.158639e+03 3.239736e+02 -3.576339 3.967260e-04
## house.age -2.895541e-02 2.101116e-03 -13.780967 6.611972e-35
## distance.to.MRT -5.908531e-04 4.583443e-05 -12.891031 1.873129e-31
## stores.number 6.768070e-02 1.034982e-02 6.539313 2.155294e-10
## latitude 2.966045e+01 2.495818e+00 11.884060 1.196885e-27
## longitude 3.495557e+00 2.546659e+00 1.372605 1.707416e-01\[ Y_{unit.price} = -1158.639 -0.029*X_{house.age} -0.0006*X_{distance.to.MRT} + 0.067*X_{stores.number} + 29.66*X_{latitude}+3.49*X_{longitude}\]
Homoscedasticity:
residuals <- model2$residuals
ggplot(data = data.frame(residuals = residuals), aes(x = fitted(model2), y = residuals)) +
geom_point() +
geom_smooth(method = "loess", se = FALSE, color = "blue") +
labs(title = "Residuals vs. Fitted Values", x = "Fitted Values", y = "Residuals")## `geom_smooth()` using formula = 'y ~ x'
qqnorm(residuals)
# QQ-plot
qqnorm(residuals)
qqline(residuals)
# Shapiro-Wilk test
shapiro.test(residuals)##
## Shapiro-Wilk normality test
##
## data: residuals
## W = 0.99652, p-value = 0.6245The p-value is greater than significance level, so fail to reject the null hypothesis, meaning that there is strong evidence to suggest normality.
Multicollinearity:
vif(model2)## house.age distance.to.MRT stores.number latitude longitude
## 1.012439 1.963795 1.479819 1.131224 1.443359vif(model1)## house.age distance.to.MRT stores.number latitude longitude
## 1.014249 4.282985 1.613339 1.599017 2.923881Model Selection:
p=5
models =regsubsets(unit.price^0.5~., data =data_no_outlier, nvmax =p)
summary(models)## Subset selection object
## Call: regsubsets.formula(unit.price^0.5 ~ ., data = data_no_outlier,
## nvmax = p)
## 5 Variables (and intercept)
## Forced in Forced out
## house.age FALSE FALSE
## distance.to.MRT FALSE FALSE
## stores.number FALSE FALSE
## latitude FALSE FALSE
## longitude FALSE FALSE
## 1 subsets of each size up to 5
## Selection Algorithm: exhaustive
## house.age distance.to.MRT stores.number latitude longitude
## 1 ( 1 ) " " "*" " " " " " "
## 2 ( 1 ) "*" "*" " " " " " "
## 3 ( 1 ) "*" "*" " " "*" " "
## 4 ( 1 ) "*" "*" "*" "*" " "
## 5 ( 1 ) "*" "*" "*" "*" "*"m1 <- lm(unit.price^0.5~distance.to.MRT, data =data_no_outlier)
m2 <- lm(unit.price^0.5~house.age+distance.to.MRT, data =data_no_outlier)
m3 <- lm(unit.price^0.5~house.age+distance.to.MRT+latitude, data =data_no_outlier)
m4 <- lm(unit.price^0.5~house.age+distance.to.MRT+stores.number+latitude, data=data_no_outlier)
m5 <- lm(unit.price^0.5~house.age+distance.to.MRT+stores.number+latitude+longitude, data =data_no_outlier)# Create a vector of model names
model_names <- c("m1", "m2", "m3", "m4", "m5")
# Create an empty data frame to store AIC values
aic_data <- data.frame(Model = character(length(model_names)), AIC = numeric(length(model_names)))
# Calculate and store AIC values for each model
for (i in 1:length(model_names)) {
model <- get(model_names[i]) # Get the model by name
aic_value <- AIC(model)
aic_data[i, ] <- c(model_names[i], aic_value)
}
# Display the table of AIC values
print(aic_data)## Model AIC
## 1 m1 714.114574310383
## 2 m2 627.925936238496
## 3 m3 502.27481418991
## 4 m4 463.809226197423
## 5 m5 463.898403702297result.sum = summary(models)criteria <- data.frame(Nvar = 1:(p),
R2 = result.sum$rsq,
R2adj = result.sum$adjr2,
CP = result.sum$cp,
BIC = result.sum$bic)
criteria <- cbind(criteria, AIC = as.numeric(aic_data$AIC))
print(criteria)## Nvar R2 R2adj CP BIC AIC
## 1 1 0.4796386 0.4781891 368.889675 -224.0389 714.1146
## 2 2 0.5924215 0.5901445 213.560759 -306.3387 627.9259
## 3 3 0.7138176 0.7114127 46.216537 -428.1009 502.2748
## 4 4 0.7441641 0.7412895 5.884046 -462.6776 463.8092
## 5 5 0.7455146 0.7419303 6.000000 -458.6996 463.8984ggplot(data = criteria, aes(x = Nvar)) +
geom_line(aes(y = R2), color = "red") +
labs(title = "R2")
ggplot(data = criteria, aes(x = Nvar)) +
geom_line(aes(y = R2adj), color = "green") +
labs(title = "R2adj")
ggplot(data = criteria, aes(x = Nvar)) +
geom_line(aes(y = CP), color = "purple") +
labs(title = "CP")
ggplot(data = criteria, aes(x = Nvar)) +
geom_line(aes(y = BIC), color = "orange") +
labs(title = "BIC")
ggplot(data = criteria, aes(x = Nvar)) +
geom_line(aes(y = AIC), color = "blue") +
labs(title = "AIC")
##Estimated best subset by each criterion >
which.best.subset = data.frame(R2 = which.max(result.sum$rsq),
R2adj = which.max(result.sum$adjr2),
CP = which.min(result.sum$cp),
BIC = which.min(result.sum$bic),
AIC = which.min(criteria$AIC))
which.best.subset## R2 R2adj CP BIC AIC
## 1 5 5 4 4 4model3 <- lm(unit.price^0.5 ~ house.age+ distance.to.MRT+ stores.number+ latitude, data=data_no_outlier)
summary(model3)##
## Call:
## lm(formula = unit.price^0.5 ~ house.age + distance.to.MRT + stores.number +
## latitude, data = data_no_outlier)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.28420 -0.28787 -0.00941 0.28640 1.67750
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -7.221e+02 6.181e+01 -11.682 < 2e-16 ***
## house.age -2.900e-02 2.103e-03 -13.788 < 2e-16 ***
## distance.to.MRT -6.225e-04 3.967e-05 -15.694 < 2e-16 ***
## stores.number 6.732e-02 1.036e-02 6.498 2.74e-10 ***
## latitude 2.919e+01 2.476e+00 11.793 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4556 on 356 degrees of freedom
## Multiple R-squared: 0.7442, Adjusted R-squared: 0.7413
## F-statistic: 258.9 on 4 and 356 DF, p-value: < 2.2e-16s3 <- summary(model3)
data.frame(s3$coefficients)## Estimate Std..Error t.value Pr...t..
## (Intercept) -7.220997e+02 6.181090e+01 -11.682399 6.525818e-27
## house.age -2.900191e-02 2.103449e-03 -13.787784 5.970874e-35
## distance.to.MRT -6.224931e-04 3.966536e-05 -15.693619 1.482819e-42
## stores.number 6.731745e-02 1.035927e-02 6.498279 2.742849e-10
## latitude 2.919293e+01 2.475535e+00 11.792573 2.551752e-27stepwise selection
#backward stepwise selection
Full = lm(unit.price^0.5~., data =data_no_outlier) #includes all predictors
backward = step(Full, direction='backward', scope=formula(Full), trace=0)
backward$anova## Step Df Deviance Resid. Df Resid. Dev AIC
## 1 NA NA 355 73.49790 -562.5752
## 2 - longitude 1 0.3900659 356 73.88797 -562.6644#Forward stepwise selection
Empty =lm(unit.price^0.5 ~ 1, data=data_no_outlier) # 1 means only intercept
forward =step(Empty, direction='forward', scope=formula(Full), trace=0) #results of forward selection
forward$anova## Step Df Deviance Resid. Df Resid. Dev AIC
## 1 NA NA 360 288.80995 -78.54238
## 2 + distance.to.MRT -1 138.524406 359 150.28554 -312.35905
## 3 + house.age -1 32.572803 358 117.71274 -398.54768
## 4 + latitude -1 35.060409 357 82.65233 -524.19881
## 5 + stores.number -1 8.764364 356 73.88797 -562.66439model3 <- lm(unit.price^0.5 ~ house.age+ distance.to.MRT+ stores.number+ latitude, data=data_no_outlier)
summary(model3)##
## Call:
## lm(formula = unit.price^0.5 ~ house.age + distance.to.MRT + stores.number +
## latitude, data = data_no_outlier)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.28420 -0.28787 -0.00941 0.28640 1.67750
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -7.221e+02 6.181e+01 -11.682 < 2e-16 ***
## house.age -2.900e-02 2.103e-03 -13.788 < 2e-16 ***
## distance.to.MRT -6.225e-04 3.967e-05 -15.694 < 2e-16 ***
## stores.number 6.732e-02 1.036e-02 6.498 2.74e-10 ***
## latitude 2.919e+01 2.476e+00 11.793 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4556 on 356 degrees of freedom
## Multiple R-squared: 0.7442, Adjusted R-squared: 0.7413
## F-statistic: 258.9 on 4 and 356 DF, p-value: < 2.2e-16model3$coefficients## (Intercept) house.age distance.to.MRT stores.number latitude
## -7.220997e+02 -2.900191e-02 -6.224931e-04 6.731745e-02 2.919293e+01Final Model
\[ Y_{unit.price} = -722.1 -0.029*X_{house.age} - 0.0006*X_{distance.to.MRT} + 0.067*X_{stores.number} + 29.19*X_{latitude}\]
model validation
library(caret)## Loading required package: latticelibrary(lattice)
train_control<- trainControl(method="cv", number= 5, savePredictions = TRUE)
model<- train(unit.price^0.5 ~ house.age+ distance.to.MRT+ stores.number+ latitude, data=data_no_outlier, trControl=train_control, method="lm")
print(model)## Linear Regression
##
## 361 samples
## 4 predictor
##
## No pre-processing
## Resampling: Cross-Validated (5 fold)
## Summary of sample sizes: 289, 289, 289, 289, 288
## Resampling results:
##
## RMSE Rsquared MAE
## 0.4516243 0.7410325 0.3604843
##
## Tuning parameter 'intercept' was held constant at a value of TRUEmodel$pred## pred obs rowIndex intercept Resample
## 1 6.951834 6.877500 3 TRUE Fold1
## 2 5.296814 5.665686 6 TRUE Fold1
## 3 7.208741 6.906519 19 TRUE Fold1
## 4 5.354583 5.196152 25 TRUE Fold1
## 5 6.330910 6.855655 28 TRUE Fold1
## 6 6.870507 6.906519 36 TRUE Fold1
## 7 6.830714 7.341662 40 TRUE Fold1
## 8 6.575637 6.480741 42 TRUE Fold1
## 9 6.357876 6.236986 46 TRUE Fold1
## 10 6.883062 7.190271 47 TRUE Fold1
## 11 5.003339 5.263079 54 TRUE Fold1
## 12 6.987287 7.120393 58 TRUE Fold1
## 13 5.774912 6.387488 63 TRUE Fold1
## 14 5.183633 5.431390 66 TRUE Fold1
## 15 4.907555 5.059644 68 TRUE Fold1
## 16 6.583696 6.610598 75 TRUE Fold1
## 17 6.735158 6.760178 92 TRUE Fold1
## 18 5.898236 5.522681 93 TRUE Fold1
## 19 6.993335 6.862944 95 TRUE Fold1
## 20 5.762564 5.839521 97 TRUE Fold1
## 21 5.401808 5.329165 98 TRUE Fold1
## 22 5.185803 4.806246 101 TRUE Fold1
## 23 6.761303 7.300685 102 TRUE Fold1
## 24 5.223152 5.531727 104 TRUE Fold1
## 25 6.984392 6.971370 111 TRUE Fold1
## 26 6.730166 7.224957 130 TRUE Fold1
## 27 6.384934 6.300794 132 TRUE Fold1
## 28 7.419245 6.685806 134 TRUE Fold1
## 29 6.395391 5.966574 138 TRUE Fold1
## 30 6.730166 7.429670 144 TRUE Fold1
## 31 6.045453 5.924525 151 TRUE Fold1
## 32 5.872489 6.041523 153 TRUE Fold1
## 33 6.352882 6.526868 156 TRUE Fold1
## 34 7.371048 7.476630 157 TRUE Fold1
## 35 5.126792 4.857983 158 TRUE Fold1
## 36 6.275309 6.503845 164 TRUE Fold1
## 37 6.224457 6.148170 165 TRUE Fold1
## 38 6.999539 7.021396 167 TRUE Fold1
## 39 6.180475 6.049793 169 TRUE Fold1
## 40 6.586105 5.621388 172 TRUE Fold1
## 41 5.499607 5.049752 173 TRUE Fold1
## 42 6.957624 6.774954 174 TRUE Fold1
## 43 6.389604 6.395311 181 TRUE Fold1
## 44 6.855577 6.595453 183 TRUE Fold1
## 45 7.368493 7.615773 185 TRUE Fold1
## 46 5.473564 6.300794 188 TRUE Fold1
## 47 6.457445 6.387488 189 TRUE Fold1
## 48 4.603492 4.358899 199 TRUE Fold1
## 49 5.401808 5.779273 200 TRUE Fold1
## 50 6.375711 6.371813 213 TRUE Fold1
## 51 5.459861 4.722288 215 TRUE Fold1
## 52 6.058681 5.477226 216 TRUE Fold1
## 53 6.256393 6.625708 222 TRUE Fold1
## 54 4.844198 4.939636 226 TRUE Fold1
## 55 6.366561 6.363961 235 TRUE Fold1
## 56 6.958184 6.737952 243 TRUE Fold1
## 57 6.586591 5.865151 247 TRUE Fold1
## 58 6.840037 7.375636 253 TRUE Fold1
## 59 6.656068 6.519202 255 TRUE Fold1
## 60 6.854143 6.074537 261 TRUE Fold1
## 61 4.555311 4.370355 270 TRUE Fold1
## 62 6.488650 6.519202 282 TRUE Fold1
## 63 5.383428 5.594640 283 TRUE Fold1
## 64 6.097623 6.041523 290 TRUE Fold1
## 65 6.069309 5.966574 291 TRUE Fold1
## 66 6.371647 6.024948 293 TRUE Fold1
## 67 6.800138 6.855655 303 TRUE Fold1
## 68 4.843296 4.979960 319 TRUE Fold1
## 69 4.916239 4.774935 322 TRUE Fold1
## 70 7.054790 7.035624 327 TRUE Fold1
## 71 6.197408 6.348228 335 TRUE Fold1
## 72 5.282340 5.263079 350 TRUE Fold1
## 73 6.915918 6.156298 1 TRUE Fold2
## 74 6.944118 7.402702 4 TRUE Fold2
## 75 7.065618 6.565059 5 TRUE Fold2
## 76 5.675951 6.434283 10 TRUE Fold2
## 77 5.878198 7.106335 15 TRUE Fold2
## 78 6.612483 6.503845 18 TRUE Fold2
## 79 5.719994 6.228965 24 TRUE Fold2
## 80 5.255436 5.224940 33 TRUE Fold2
## 81 5.198587 5.029911 35 TRUE Fold2
## 82 6.020062 6.188699 41 TRUE Fold2
## 83 6.257539 6.648308 43 TRUE Fold2
## 84 6.836969 7.314369 50 TRUE Fold2
## 85 6.230512 6.511528 51 TRUE Fold2
## 86 6.303785 6.655825 57 TRUE Fold2
## 87 6.337594 6.016644 60 TRUE Fold2
## 88 5.635243 5.458938 69 TRUE Fold2
## 89 4.405682 4.207137 74 TRUE Fold2
## 90 7.248825 7.127412 76 TRUE Fold2
## 91 5.428064 5.196152 77 TRUE Fold2
## 92 6.147245 6.928203 78 TRUE Fold2
## 93 4.779446 4.669047 81 TRUE Fold2
## 94 4.561722 4.012481 82 TRUE Fold2
## 95 7.365112 7.886698 88 TRUE Fold2
## 96 5.192522 5.157519 96 TRUE Fold2
## 97 7.148063 7.416198 112 TRUE Fold2
## 98 5.447973 5.540758 115 TRUE Fold2
## 99 5.445168 4.560702 119 TRUE Fold2
## 100 6.871554 6.841053 120 TRUE Fold2
## 101 6.361791 6.519202 123 TRUE Fold2
## 102 6.860901 7.169379 124 TRUE Fold2
## 103 6.388854 6.395311 136 TRUE Fold2
## 104 6.766862 7.449832 143 TRUE Fold2
## 105 5.841179 6.115554 147 TRUE Fold2
## 106 5.030863 4.847680 148 TRUE Fold2
## 107 4.406721 4.669047 159 TRUE Fold2
## 108 6.260198 6.942622 170 TRUE Fold2
## 109 6.104730 6.252999 171 TRUE Fold2
## 110 7.059554 7.224957 182 TRUE Fold2
## 111 6.340565 6.618157 190 TRUE Fold2
## 112 6.145653 6.204837 192 TRUE Fold2
## 113 6.379423 5.692100 201 TRUE Fold2
## 114 5.280608 4.888763 202 TRUE Fold2
## 115 6.158840 6.244998 205 TRUE Fold2
## 116 5.444402 5.366563 208 TRUE Fold2
## 117 6.659657 6.434283 209 TRUE Fold2
## 118 5.291915 4.806246 214 TRUE Fold2
## 119 7.309540 7.197222 220 TRUE Fold2
## 120 6.869998 7.280110 227 TRUE Fold2
## 121 6.047623 6.371813 229 TRUE Fold2
## 122 6.400574 6.172520 230 TRUE Fold2
## 123 6.740933 6.633250 241 TRUE Fold2
## 124 7.437965 6.693280 244 TRUE Fold2
## 125 7.380273 7.503333 248 TRUE Fold2
## 126 5.878602 5.735852 249 TRUE Fold2
## 127 6.803142 7.141428 250 TRUE Fold2
## 128 5.418616 6.196773 264 TRUE Fold2
## 129 5.031210 4.969909 271 TRUE Fold2
## 130 6.259565 6.123724 277 TRUE Fold2
## 131 5.560743 5.186521 279 TRUE Fold2
## 132 6.557964 6.140033 280 TRUE Fold2
## 133 6.628144 6.058052 286 TRUE Fold2
## 134 5.693946 6.549809 295 TRUE Fold2
## 135 6.869998 6.488451 323 TRUE Fold2
## 136 6.845138 7.224957 326 TRUE Fold2
## 137 5.958600 6.115554 331 TRUE Fold2
## 138 5.966024 5.941380 342 TRUE Fold2
## 139 6.556415 6.348228 343 TRUE Fold2
## 140 6.207217 5.958188 349 TRUE Fold2
## 141 7.142067 6.363961 355 TRUE Fold2
## 142 4.800086 4.722288 356 TRUE Fold2
## 143 7.389370 7.071068 358 TRUE Fold2
## 144 6.778991 7.245688 360 TRUE Fold2
## 145 6.197394 6.348228 7 TRUE Fold3
## 146 6.797398 6.833740 8 TRUE Fold3
## 147 5.330286 4.701064 9 TRUE Fold3
## 148 7.010002 6.920983 23 TRUE Fold3
## 149 7.188916 7.496666 26 TRUE Fold3
## 150 6.872650 7.556454 29 TRUE Fold3
## 151 6.825312 7.021396 31 TRUE Fold3
## 152 7.168833 7.422937 32 TRUE Fold3
## 153 6.901630 6.797058 37 TRUE Fold3
## 154 5.604568 5.890671 38 TRUE Fold3
## 155 6.244646 6.473021 49 TRUE Fold3
## 156 7.117171 7.949843 53 TRUE Fold3
## 157 6.787350 7.536577 59 TRUE Fold3
## 158 7.330127 7.681146 62 TRUE Fold3
## 159 6.342354 6.024948 64 TRUE Fold3
## 160 6.917891 6.737952 79 TRUE Fold3
## 161 6.801972 7.197222 84 TRUE Fold3
## 162 6.895782 7.141428 87 TRUE Fold3
## 163 6.436158 6.276942 100 TRUE Fold3
## 164 6.264556 6.811755 103 TRUE Fold3
## 165 6.351551 5.594640 106 TRUE Fold3
## 166 6.801124 6.745369 109 TRUE Fold3
## 167 6.507254 6.123724 114 TRUE Fold3
## 168 7.120147 6.284903 117 TRUE Fold3
## 169 6.048440 6.496153 118 TRUE Fold3
## 170 5.991704 5.375872 125 TRUE Fold3
## 171 6.216752 6.123724 126 TRUE Fold3
## 172 5.194029 5.329165 128 TRUE Fold3
## 173 6.085788 6.964194 133 TRUE Fold3
## 174 5.965488 5.531727 145 TRUE Fold3
## 175 7.237997 6.723095 152 TRUE Fold3
## 176 6.579142 6.480741 154 TRUE Fold3
## 177 6.673604 6.058052 155 TRUE Fold3
## 178 5.316172 4.636809 160 TRUE Fold3
## 179 4.197665 4.690416 162 TRUE Fold3
## 180 5.933509 6.534524 166 TRUE Fold3
## 181 5.711136 5.612486 175 TRUE Fold3
## 182 6.243555 6.371813 206 TRUE Fold3
## 183 6.668758 6.942622 211 TRUE Fold3
## 184 6.460899 5.147815 221 TRUE Fold3
## 185 5.634026 5.366563 224 TRUE Fold3
## 186 5.333210 4.868265 231 TRUE Fold3
## 187 4.854253 5.263079 240 TRUE Fold3
## 188 5.535624 6.082763 252 TRUE Fold3
## 189 6.038870 4.949747 254 TRUE Fold3
## 190 4.938642 4.669047 257 TRUE Fold3
## 191 5.755339 5.338539 259 TRUE Fold3
## 192 6.817918 6.789698 260 TRUE Fold3
## 193 4.795116 4.816638 263 TRUE Fold3
## 194 6.530146 7.085196 267 TRUE Fold3
## 195 6.568497 5.753260 281 TRUE Fold3
## 196 6.794141 7.880355 285 TRUE Fold3
## 197 6.807820 6.196773 289 TRUE Fold3
## 198 5.862208 5.558777 292 TRUE Fold3
## 199 6.263291 6.082763 296 TRUE Fold3
## 200 4.739040 6.418723 299 TRUE Fold3
## 201 7.190594 6.700746 311 TRUE Fold3
## 202 6.885218 6.862944 314 TRUE Fold3
## 203 6.173987 5.753260 317 TRUE Fold3
## 204 4.940325 5.431390 318 TRUE Fold3
## 205 6.458844 7.190271 324 TRUE Fold3
## 206 6.122518 6.442049 325 TRUE Fold3
## 207 6.894608 7.536577 330 TRUE Fold3
## 208 7.298402 8.348653 332 TRUE Fold3
## 209 7.314983 6.826419 336 TRUE Fold3
## 210 4.968874 5.059644 338 TRUE Fold3
## 211 6.295744 6.212890 340 TRUE Fold3
## 212 5.318369 4.795832 347 TRUE Fold3
## 213 6.574346 5.338539 351 TRUE Fold3
## 214 6.849076 6.300794 352 TRUE Fold3
## 215 6.106814 6.099180 354 TRUE Fold3
## 216 6.802257 6.371813 359 TRUE Fold3
## 217 7.331769 7.622336 11 TRUE Fold4
## 218 4.709024 4.878524 13 TRUE Fold4
## 219 6.649485 5.856620 14 TRUE Fold4
## 220 5.287338 5.412947 20 TRUE Fold4
## 221 5.108475 4.959839 22 TRUE Fold4
## 222 5.628381 5.848077 30 TRUE Fold4
## 223 5.164453 4.785394 34 TRUE Fold4
## 224 6.454466 5.839521 39 TRUE Fold4
## 225 5.347667 5.196152 45 TRUE Fold4
## 226 5.267416 4.615192 52 TRUE Fold4
## 227 6.841402 7.416198 55 TRUE Fold4
## 228 7.471843 7.375636 65 TRUE Fold4
## 229 4.972173 5.147815 70 TRUE Fold4
## 230 6.753975 6.935416 73 TRUE Fold4
## 231 7.328881 7.713624 85 TRUE Fold4
## 232 6.069099 5.882176 86 TRUE Fold4
## 233 6.566331 6.180615 89 TRUE Fold4
## 234 6.563112 5.735852 90 TRUE Fold4
## 235 6.966119 7.375636 91 TRUE Fold4
## 236 6.881800 7.720104 105 TRUE Fold4
## 237 6.336040 6.928203 107 TRUE Fold4
## 238 6.306860 5.700877 108 TRUE Fold4
## 239 7.008646 7.576279 110 TRUE Fold4
## 240 6.231709 6.595453 122 TRUE Fold4
## 241 6.336040 6.332456 127 TRUE Fold4
## 242 7.007272 6.745369 129 TRUE Fold4
## 243 6.981124 6.572671 131 TRUE Fold4
## 244 4.546106 4.277850 137 TRUE Fold4
## 245 6.896430 7.602631 141 TRUE Fold4
## 246 6.476649 6.587868 146 TRUE Fold4
## 247 7.323105 7.622336 150 TRUE Fold4
## 248 6.463594 6.655825 163 TRUE Fold4
## 249 5.983534 5.882176 168 TRUE Fold4
## 250 6.910678 6.789698 176 TRUE Fold4
## 251 5.674805 5.848077 179 TRUE Fold4
## 252 6.185526 6.503845 194 TRUE Fold4
## 253 6.290554 6.782330 195 TRUE Fold4
## 254 6.966119 7.000000 196 TRUE Fold4
## 255 5.453311 6.826419 198 TRUE Fold4
## 256 6.664311 6.268971 203 TRUE Fold4
## 257 5.612552 5.449771 207 TRUE Fold4
## 258 5.381057 5.630275 228 TRUE Fold4
## 259 6.007142 6.410928 232 TRUE Fold4
## 260 5.389906 5.059644 245 TRUE Fold4
## 261 5.278142 4.847680 246 TRUE Fold4
## 262 6.561456 6.172520 256 TRUE Fold4
## 263 6.837474 6.449806 274 TRUE Fold4
## 264 5.829107 5.224940 275 TRUE Fold4
## 265 6.652760 6.480741 276 TRUE Fold4
## 266 7.463179 7.056912 278 TRUE Fold4
## 267 6.016666 6.172520 284 TRUE Fold4
## 268 5.653897 4.857983 287 TRUE Fold4
## 269 5.624796 6.292853 288 TRUE Fold4
## 270 7.349096 7.314369 297 TRUE Fold4
## 271 6.777805 6.156298 300 TRUE Fold4
## 272 5.355505 5.549775 301 TRUE Fold4
## 273 7.126629 7.328028 302 TRUE Fold4
## 274 6.347591 6.503845 304 TRUE Fold4
## 275 5.364957 5.347897 305 TRUE Fold4
## 276 4.583626 5.069517 306 TRUE Fold4
## 277 5.180670 5.486347 308 TRUE Fold4
## 278 6.620168 6.730527 310 TRUE Fold4
## 279 6.966119 6.715653 312 TRUE Fold4
## 280 4.736728 4.969909 313 TRUE Fold4
## 281 6.576214 6.324555 315 TRUE Fold4
## 282 6.857006 6.928203 316 TRUE Fold4
## 283 6.342262 6.565059 321 TRUE Fold4
## 284 6.953995 7.300685 333 TRUE Fold4
## 285 6.777805 7.436397 337 TRUE Fold4
## 286 5.089681 5.224940 339 TRUE Fold4
## 287 6.652373 5.674504 346 TRUE Fold4
## 288 6.563112 6.107373 348 TRUE Fold4
## 289 6.968540 6.496153 2 TRUE Fold5
## 290 6.358410 6.268971 12 TRUE Fold5
## 291 7.260664 8.372574 16 TRUE Fold5
## 292 6.386949 6.115554 17 TRUE Fold5
## 293 6.973673 7.183314 21 TRUE Fold5
## 294 6.302189 5.796551 27 TRUE Fold5
## 295 4.483077 4.549725 44 TRUE Fold5
## 296 4.673501 3.701351 48 TRUE Fold5
## 297 5.560558 5.029911 56 TRUE Fold5
## 298 6.973899 6.480741 61 TRUE Fold5
## 299 5.878826 6.066300 67 TRUE Fold5
## 300 6.576997 6.348228 71 TRUE Fold5
## 301 5.971040 6.066300 72 TRUE Fold5
## 302 6.187816 6.572671 80 TRUE Fold5
## 303 5.827760 6.403124 83 TRUE Fold5
## 304 7.260664 8.426150 94 TRUE Fold5
## 305 6.789706 7.183314 99 TRUE Fold5
## 306 6.399746 6.403124 113 TRUE Fold5
## 307 6.253743 6.123724 116 TRUE Fold5
## 308 6.505107 6.884766 121 TRUE Fold5
## 309 5.216868 5.375872 135 TRUE Fold5
## 310 6.874758 6.276942 139 TRUE Fold5
## 311 6.420416 6.115554 140 TRUE Fold5
## 312 6.341521 6.292853 142 TRUE Fold5
## 313 7.143617 7.668116 149 TRUE Fold5
## 314 4.878319 5.069517 161 TRUE Fold5
## 315 5.009035 5.157519 177 TRUE Fold5
## 316 6.993922 6.633250 178 TRUE Fold5
## 317 5.230721 5.118594 180 TRUE Fold5
## 318 5.837497 5.576737 184 TRUE Fold5
## 319 5.333236 4.571652 186 TRUE Fold5
## 320 6.627881 6.935416 187 TRUE Fold5
## 321 6.515399 6.340347 191 TRUE Fold5
## 322 6.664237 6.964194 193 TRUE Fold5
## 323 6.587496 6.340347 197 TRUE Fold5
## 324 7.506855 7.867655 204 TRUE Fold5
## 325 5.481483 5.779273 210 TRUE Fold5
## 326 6.631216 6.387488 212 TRUE Fold5
## 327 4.671941 3.714835 217 TRUE Fold5
## 328 7.310344 7.259477 218 TRUE Fold5
## 329 4.888091 5.089204 219 TRUE Fold5
## 330 7.260664 7.956130 223 TRUE Fold5
## 331 5.712378 5.540758 225 TRUE Fold5
## 332 6.719603 6.332456 233 TRUE Fold5
## 333 5.667947 4.795832 234 TRUE Fold5
## 334 6.577857 5.412947 236 TRUE Fold5
## 335 6.364092 6.403124 237 TRUE Fold5
## 336 7.168639 7.049823 238 TRUE Fold5
## 337 5.893562 5.830952 239 TRUE Fold5
## 338 5.995581 5.576737 242 TRUE Fold5
## 339 6.953310 6.670832 251 TRUE Fold5
## 340 6.665859 5.839521 258 TRUE Fold5
## 341 5.604175 5.974948 262 TRUE Fold5
## 342 5.350159 5.422177 265 TRUE Fold5
## 343 6.889586 7.416198 266 TRUE Fold5
## 344 4.173207 4.969909 268 TRUE Fold5
## 345 6.884045 7.280110 269 TRUE Fold5
## 346 6.305209 6.496153 272 TRUE Fold5
## 347 6.784165 6.542171 273 TRUE Fold5
## 348 7.279933 7.099296 294 TRUE Fold5
## 349 6.412523 6.826419 298 TRUE Fold5
## 350 5.361428 5.594640 307 TRUE Fold5
## 351 7.138076 7.791020 309 TRUE Fold5
## 352 5.255064 4.571652 320 TRUE Fold5
## 353 5.299159 4.878524 328 TRUE Fold5
## 354 5.330002 5.522681 329 TRUE Fold5
## 355 7.475738 6.877500 334 TRUE Fold5
## 356 4.990159 5.594640 341 TRUE Fold5
## 357 6.316991 6.519202 344 TRUE Fold5
## 358 5.383585 5.648008 345 TRUE Fold5
## 359 6.884045 6.418723 353 TRUE Fold5
## 360 4.953175 5.300943 357 TRUE Fold5
## 361 7.293720 7.993748 361 TRUE Fold5model$pred## pred obs rowIndex intercept Resample
## 1 6.951834 6.877500 3 TRUE Fold1
## 2 5.296814 5.665686 6 TRUE Fold1
## 3 7.208741 6.906519 19 TRUE Fold1
## 4 5.354583 5.196152 25 TRUE Fold1
## 5 6.330910 6.855655 28 TRUE Fold1
## 6 6.870507 6.906519 36 TRUE Fold1
## 7 6.830714 7.341662 40 TRUE Fold1
## 8 6.575637 6.480741 42 TRUE Fold1
## 9 6.357876 6.236986 46 TRUE Fold1
## 10 6.883062 7.190271 47 TRUE Fold1
## 11 5.003339 5.263079 54 TRUE Fold1
## 12 6.987287 7.120393 58 TRUE Fold1
## 13 5.774912 6.387488 63 TRUE Fold1
## 14 5.183633 5.431390 66 TRUE Fold1
## 15 4.907555 5.059644 68 TRUE Fold1
## 16 6.583696 6.610598 75 TRUE Fold1
## 17 6.735158 6.760178 92 TRUE Fold1
## 18 5.898236 5.522681 93 TRUE Fold1
## 19 6.993335 6.862944 95 TRUE Fold1
## 20 5.762564 5.839521 97 TRUE Fold1
## 21 5.401808 5.329165 98 TRUE Fold1
## 22 5.185803 4.806246 101 TRUE Fold1
## 23 6.761303 7.300685 102 TRUE Fold1
## 24 5.223152 5.531727 104 TRUE Fold1
## 25 6.984392 6.971370 111 TRUE Fold1
## 26 6.730166 7.224957 130 TRUE Fold1
## 27 6.384934 6.300794 132 TRUE Fold1
## 28 7.419245 6.685806 134 TRUE Fold1
## 29 6.395391 5.966574 138 TRUE Fold1
## 30 6.730166 7.429670 144 TRUE Fold1
## 31 6.045453 5.924525 151 TRUE Fold1
## 32 5.872489 6.041523 153 TRUE Fold1
## 33 6.352882 6.526868 156 TRUE Fold1
## 34 7.371048 7.476630 157 TRUE Fold1
## 35 5.126792 4.857983 158 TRUE Fold1
## 36 6.275309 6.503845 164 TRUE Fold1
## 37 6.224457 6.148170 165 TRUE Fold1
## 38 6.999539 7.021396 167 TRUE Fold1
## 39 6.180475 6.049793 169 TRUE Fold1
## 40 6.586105 5.621388 172 TRUE Fold1
## 41 5.499607 5.049752 173 TRUE Fold1
## 42 6.957624 6.774954 174 TRUE Fold1
## 43 6.389604 6.395311 181 TRUE Fold1
## 44 6.855577 6.595453 183 TRUE Fold1
## 45 7.368493 7.615773 185 TRUE Fold1
## 46 5.473564 6.300794 188 TRUE Fold1
## 47 6.457445 6.387488 189 TRUE Fold1
## 48 4.603492 4.358899 199 TRUE Fold1
## 49 5.401808 5.779273 200 TRUE Fold1
## 50 6.375711 6.371813 213 TRUE Fold1
## 51 5.459861 4.722288 215 TRUE Fold1
## 52 6.058681 5.477226 216 TRUE Fold1
## 53 6.256393 6.625708 222 TRUE Fold1
## 54 4.844198 4.939636 226 TRUE Fold1
## 55 6.366561 6.363961 235 TRUE Fold1
## 56 6.958184 6.737952 243 TRUE Fold1
## 57 6.586591 5.865151 247 TRUE Fold1
## 58 6.840037 7.375636 253 TRUE Fold1
## 59 6.656068 6.519202 255 TRUE Fold1
## 60 6.854143 6.074537 261 TRUE Fold1
## 61 4.555311 4.370355 270 TRUE Fold1
## 62 6.488650 6.519202 282 TRUE Fold1
## 63 5.383428 5.594640 283 TRUE Fold1
## 64 6.097623 6.041523 290 TRUE Fold1
## 65 6.069309 5.966574 291 TRUE Fold1
## 66 6.371647 6.024948 293 TRUE Fold1
## 67 6.800138 6.855655 303 TRUE Fold1
## 68 4.843296 4.979960 319 TRUE Fold1
## 69 4.916239 4.774935 322 TRUE Fold1
## 70 7.054790 7.035624 327 TRUE Fold1
## 71 6.197408 6.348228 335 TRUE Fold1
## 72 5.282340 5.263079 350 TRUE Fold1
## 73 6.915918 6.156298 1 TRUE Fold2
## 74 6.944118 7.402702 4 TRUE Fold2
## 75 7.065618 6.565059 5 TRUE Fold2
## 76 5.675951 6.434283 10 TRUE Fold2
## 77 5.878198 7.106335 15 TRUE Fold2
## 78 6.612483 6.503845 18 TRUE Fold2
## 79 5.719994 6.228965 24 TRUE Fold2
## 80 5.255436 5.224940 33 TRUE Fold2
## 81 5.198587 5.029911 35 TRUE Fold2
## 82 6.020062 6.188699 41 TRUE Fold2
## 83 6.257539 6.648308 43 TRUE Fold2
## 84 6.836969 7.314369 50 TRUE Fold2
## 85 6.230512 6.511528 51 TRUE Fold2
## 86 6.303785 6.655825 57 TRUE Fold2
## 87 6.337594 6.016644 60 TRUE Fold2
## 88 5.635243 5.458938 69 TRUE Fold2
## 89 4.405682 4.207137 74 TRUE Fold2
## 90 7.248825 7.127412 76 TRUE Fold2
## 91 5.428064 5.196152 77 TRUE Fold2
## 92 6.147245 6.928203 78 TRUE Fold2
## 93 4.779446 4.669047 81 TRUE Fold2
## 94 4.561722 4.012481 82 TRUE Fold2
## 95 7.365112 7.886698 88 TRUE Fold2
## 96 5.192522 5.157519 96 TRUE Fold2
## 97 7.148063 7.416198 112 TRUE Fold2
## 98 5.447973 5.540758 115 TRUE Fold2
## 99 5.445168 4.560702 119 TRUE Fold2
## 100 6.871554 6.841053 120 TRUE Fold2
## 101 6.361791 6.519202 123 TRUE Fold2
## 102 6.860901 7.169379 124 TRUE Fold2
## 103 6.388854 6.395311 136 TRUE Fold2
## 104 6.766862 7.449832 143 TRUE Fold2
## 105 5.841179 6.115554 147 TRUE Fold2
## 106 5.030863 4.847680 148 TRUE Fold2
## 107 4.406721 4.669047 159 TRUE Fold2
## 108 6.260198 6.942622 170 TRUE Fold2
## 109 6.104730 6.252999 171 TRUE Fold2
## 110 7.059554 7.224957 182 TRUE Fold2
## 111 6.340565 6.618157 190 TRUE Fold2
## 112 6.145653 6.204837 192 TRUE Fold2
## 113 6.379423 5.692100 201 TRUE Fold2
## 114 5.280608 4.888763 202 TRUE Fold2
## 115 6.158840 6.244998 205 TRUE Fold2
## 116 5.444402 5.366563 208 TRUE Fold2
## 117 6.659657 6.434283 209 TRUE Fold2
## 118 5.291915 4.806246 214 TRUE Fold2
## 119 7.309540 7.197222 220 TRUE Fold2
## 120 6.869998 7.280110 227 TRUE Fold2
## 121 6.047623 6.371813 229 TRUE Fold2
## 122 6.400574 6.172520 230 TRUE Fold2
## 123 6.740933 6.633250 241 TRUE Fold2
## 124 7.437965 6.693280 244 TRUE Fold2
## 125 7.380273 7.503333 248 TRUE Fold2
## 126 5.878602 5.735852 249 TRUE Fold2
## 127 6.803142 7.141428 250 TRUE Fold2
## 128 5.418616 6.196773 264 TRUE Fold2
## 129 5.031210 4.969909 271 TRUE Fold2
## 130 6.259565 6.123724 277 TRUE Fold2
## 131 5.560743 5.186521 279 TRUE Fold2
## 132 6.557964 6.140033 280 TRUE Fold2
## 133 6.628144 6.058052 286 TRUE Fold2
## 134 5.693946 6.549809 295 TRUE Fold2
## 135 6.869998 6.488451 323 TRUE Fold2
## 136 6.845138 7.224957 326 TRUE Fold2
## 137 5.958600 6.115554 331 TRUE Fold2
## 138 5.966024 5.941380 342 TRUE Fold2
## 139 6.556415 6.348228 343 TRUE Fold2
## 140 6.207217 5.958188 349 TRUE Fold2
## 141 7.142067 6.363961 355 TRUE Fold2
## 142 4.800086 4.722288 356 TRUE Fold2
## 143 7.389370 7.071068 358 TRUE Fold2
## 144 6.778991 7.245688 360 TRUE Fold2
## 145 6.197394 6.348228 7 TRUE Fold3
## 146 6.797398 6.833740 8 TRUE Fold3
## 147 5.330286 4.701064 9 TRUE Fold3
## 148 7.010002 6.920983 23 TRUE Fold3
## 149 7.188916 7.496666 26 TRUE Fold3
## 150 6.872650 7.556454 29 TRUE Fold3
## 151 6.825312 7.021396 31 TRUE Fold3
## 152 7.168833 7.422937 32 TRUE Fold3
## 153 6.901630 6.797058 37 TRUE Fold3
## 154 5.604568 5.890671 38 TRUE Fold3
## 155 6.244646 6.473021 49 TRUE Fold3
## 156 7.117171 7.949843 53 TRUE Fold3
## 157 6.787350 7.536577 59 TRUE Fold3
## 158 7.330127 7.681146 62 TRUE Fold3
## 159 6.342354 6.024948 64 TRUE Fold3
## 160 6.917891 6.737952 79 TRUE Fold3
## 161 6.801972 7.197222 84 TRUE Fold3
## 162 6.895782 7.141428 87 TRUE Fold3
## 163 6.436158 6.276942 100 TRUE Fold3
## 164 6.264556 6.811755 103 TRUE Fold3
## 165 6.351551 5.594640 106 TRUE Fold3
## 166 6.801124 6.745369 109 TRUE Fold3
## 167 6.507254 6.123724 114 TRUE Fold3
## 168 7.120147 6.284903 117 TRUE Fold3
## 169 6.048440 6.496153 118 TRUE Fold3
## 170 5.991704 5.375872 125 TRUE Fold3
## 171 6.216752 6.123724 126 TRUE Fold3
## 172 5.194029 5.329165 128 TRUE Fold3
## 173 6.085788 6.964194 133 TRUE Fold3
## 174 5.965488 5.531727 145 TRUE Fold3
## 175 7.237997 6.723095 152 TRUE Fold3
## 176 6.579142 6.480741 154 TRUE Fold3
## 177 6.673604 6.058052 155 TRUE Fold3
## 178 5.316172 4.636809 160 TRUE Fold3
## 179 4.197665 4.690416 162 TRUE Fold3
## 180 5.933509 6.534524 166 TRUE Fold3
## 181 5.711136 5.612486 175 TRUE Fold3
## 182 6.243555 6.371813 206 TRUE Fold3
## 183 6.668758 6.942622 211 TRUE Fold3
## 184 6.460899 5.147815 221 TRUE Fold3
## 185 5.634026 5.366563 224 TRUE Fold3
## 186 5.333210 4.868265 231 TRUE Fold3
## 187 4.854253 5.263079 240 TRUE Fold3
## 188 5.535624 6.082763 252 TRUE Fold3
## 189 6.038870 4.949747 254 TRUE Fold3
## 190 4.938642 4.669047 257 TRUE Fold3
## 191 5.755339 5.338539 259 TRUE Fold3
## 192 6.817918 6.789698 260 TRUE Fold3
## 193 4.795116 4.816638 263 TRUE Fold3
## 194 6.530146 7.085196 267 TRUE Fold3
## 195 6.568497 5.753260 281 TRUE Fold3
## 196 6.794141 7.880355 285 TRUE Fold3
## 197 6.807820 6.196773 289 TRUE Fold3
## 198 5.862208 5.558777 292 TRUE Fold3
## 199 6.263291 6.082763 296 TRUE Fold3
## 200 4.739040 6.418723 299 TRUE Fold3
## 201 7.190594 6.700746 311 TRUE Fold3
## 202 6.885218 6.862944 314 TRUE Fold3
## 203 6.173987 5.753260 317 TRUE Fold3
## 204 4.940325 5.431390 318 TRUE Fold3
## 205 6.458844 7.190271 324 TRUE Fold3
## 206 6.122518 6.442049 325 TRUE Fold3
## 207 6.894608 7.536577 330 TRUE Fold3
## 208 7.298402 8.348653 332 TRUE Fold3
## 209 7.314983 6.826419 336 TRUE Fold3
## 210 4.968874 5.059644 338 TRUE Fold3
## 211 6.295744 6.212890 340 TRUE Fold3
## 212 5.318369 4.795832 347 TRUE Fold3
## 213 6.574346 5.338539 351 TRUE Fold3
## 214 6.849076 6.300794 352 TRUE Fold3
## 215 6.106814 6.099180 354 TRUE Fold3
## 216 6.802257 6.371813 359 TRUE Fold3
## 217 7.331769 7.622336 11 TRUE Fold4
## 218 4.709024 4.878524 13 TRUE Fold4
## 219 6.649485 5.856620 14 TRUE Fold4
## 220 5.287338 5.412947 20 TRUE Fold4
## 221 5.108475 4.959839 22 TRUE Fold4
## 222 5.628381 5.848077 30 TRUE Fold4
## 223 5.164453 4.785394 34 TRUE Fold4
## 224 6.454466 5.839521 39 TRUE Fold4
## 225 5.347667 5.196152 45 TRUE Fold4
## 226 5.267416 4.615192 52 TRUE Fold4
## 227 6.841402 7.416198 55 TRUE Fold4
## 228 7.471843 7.375636 65 TRUE Fold4
## 229 4.972173 5.147815 70 TRUE Fold4
## 230 6.753975 6.935416 73 TRUE Fold4
## 231 7.328881 7.713624 85 TRUE Fold4
## 232 6.069099 5.882176 86 TRUE Fold4
## 233 6.566331 6.180615 89 TRUE Fold4
## 234 6.563112 5.735852 90 TRUE Fold4
## 235 6.966119 7.375636 91 TRUE Fold4
## 236 6.881800 7.720104 105 TRUE Fold4
## 237 6.336040 6.928203 107 TRUE Fold4
## 238 6.306860 5.700877 108 TRUE Fold4
## 239 7.008646 7.576279 110 TRUE Fold4
## 240 6.231709 6.595453 122 TRUE Fold4
## 241 6.336040 6.332456 127 TRUE Fold4
## 242 7.007272 6.745369 129 TRUE Fold4
## 243 6.981124 6.572671 131 TRUE Fold4
## 244 4.546106 4.277850 137 TRUE Fold4
## 245 6.896430 7.602631 141 TRUE Fold4
## 246 6.476649 6.587868 146 TRUE Fold4
## 247 7.323105 7.622336 150 TRUE Fold4
## 248 6.463594 6.655825 163 TRUE Fold4
## 249 5.983534 5.882176 168 TRUE Fold4
## 250 6.910678 6.789698 176 TRUE Fold4
## 251 5.674805 5.848077 179 TRUE Fold4
## 252 6.185526 6.503845 194 TRUE Fold4
## 253 6.290554 6.782330 195 TRUE Fold4
## 254 6.966119 7.000000 196 TRUE Fold4
## 255 5.453311 6.826419 198 TRUE Fold4
## 256 6.664311 6.268971 203 TRUE Fold4
## 257 5.612552 5.449771 207 TRUE Fold4
## 258 5.381057 5.630275 228 TRUE Fold4
## 259 6.007142 6.410928 232 TRUE Fold4
## 260 5.389906 5.059644 245 TRUE Fold4
## 261 5.278142 4.847680 246 TRUE Fold4
## 262 6.561456 6.172520 256 TRUE Fold4
## 263 6.837474 6.449806 274 TRUE Fold4
## 264 5.829107 5.224940 275 TRUE Fold4
## 265 6.652760 6.480741 276 TRUE Fold4
## 266 7.463179 7.056912 278 TRUE Fold4
## 267 6.016666 6.172520 284 TRUE Fold4
## 268 5.653897 4.857983 287 TRUE Fold4
## 269 5.624796 6.292853 288 TRUE Fold4
## 270 7.349096 7.314369 297 TRUE Fold4
## 271 6.777805 6.156298 300 TRUE Fold4
## 272 5.355505 5.549775 301 TRUE Fold4
## 273 7.126629 7.328028 302 TRUE Fold4
## 274 6.347591 6.503845 304 TRUE Fold4
## 275 5.364957 5.347897 305 TRUE Fold4
## 276 4.583626 5.069517 306 TRUE Fold4
## 277 5.180670 5.486347 308 TRUE Fold4
## 278 6.620168 6.730527 310 TRUE Fold4
## 279 6.966119 6.715653 312 TRUE Fold4
## 280 4.736728 4.969909 313 TRUE Fold4
## 281 6.576214 6.324555 315 TRUE Fold4
## 282 6.857006 6.928203 316 TRUE Fold4
## 283 6.342262 6.565059 321 TRUE Fold4
## 284 6.953995 7.300685 333 TRUE Fold4
## 285 6.777805 7.436397 337 TRUE Fold4
## 286 5.089681 5.224940 339 TRUE Fold4
## 287 6.652373 5.674504 346 TRUE Fold4
## 288 6.563112 6.107373 348 TRUE Fold4
## 289 6.968540 6.496153 2 TRUE Fold5
## 290 6.358410 6.268971 12 TRUE Fold5
## 291 7.260664 8.372574 16 TRUE Fold5
## 292 6.386949 6.115554 17 TRUE Fold5
## 293 6.973673 7.183314 21 TRUE Fold5
## 294 6.302189 5.796551 27 TRUE Fold5
## 295 4.483077 4.549725 44 TRUE Fold5
## 296 4.673501 3.701351 48 TRUE Fold5
## 297 5.560558 5.029911 56 TRUE Fold5
## 298 6.973899 6.480741 61 TRUE Fold5
## 299 5.878826 6.066300 67 TRUE Fold5
## 300 6.576997 6.348228 71 TRUE Fold5
## 301 5.971040 6.066300 72 TRUE Fold5
## 302 6.187816 6.572671 80 TRUE Fold5
## 303 5.827760 6.403124 83 TRUE Fold5
## 304 7.260664 8.426150 94 TRUE Fold5
## 305 6.789706 7.183314 99 TRUE Fold5
## 306 6.399746 6.403124 113 TRUE Fold5
## 307 6.253743 6.123724 116 TRUE Fold5
## 308 6.505107 6.884766 121 TRUE Fold5
## 309 5.216868 5.375872 135 TRUE Fold5
## 310 6.874758 6.276942 139 TRUE Fold5
## 311 6.420416 6.115554 140 TRUE Fold5
## 312 6.341521 6.292853 142 TRUE Fold5
## 313 7.143617 7.668116 149 TRUE Fold5
## 314 4.878319 5.069517 161 TRUE Fold5
## 315 5.009035 5.157519 177 TRUE Fold5
## 316 6.993922 6.633250 178 TRUE Fold5
## 317 5.230721 5.118594 180 TRUE Fold5
## 318 5.837497 5.576737 184 TRUE Fold5
## 319 5.333236 4.571652 186 TRUE Fold5
## 320 6.627881 6.935416 187 TRUE Fold5
## 321 6.515399 6.340347 191 TRUE Fold5
## 322 6.664237 6.964194 193 TRUE Fold5
## 323 6.587496 6.340347 197 TRUE Fold5
## 324 7.506855 7.867655 204 TRUE Fold5
## 325 5.481483 5.779273 210 TRUE Fold5
## 326 6.631216 6.387488 212 TRUE Fold5
## 327 4.671941 3.714835 217 TRUE Fold5
## 328 7.310344 7.259477 218 TRUE Fold5
## 329 4.888091 5.089204 219 TRUE Fold5
## 330 7.260664 7.956130 223 TRUE Fold5
## 331 5.712378 5.540758 225 TRUE Fold5
## 332 6.719603 6.332456 233 TRUE Fold5
## 333 5.667947 4.795832 234 TRUE Fold5
## 334 6.577857 5.412947 236 TRUE Fold5
## 335 6.364092 6.403124 237 TRUE Fold5
## 336 7.168639 7.049823 238 TRUE Fold5
## 337 5.893562 5.830952 239 TRUE Fold5
## 338 5.995581 5.576737 242 TRUE Fold5
## 339 6.953310 6.670832 251 TRUE Fold5
## 340 6.665859 5.839521 258 TRUE Fold5
## 341 5.604175 5.974948 262 TRUE Fold5
## 342 5.350159 5.422177 265 TRUE Fold5
## 343 6.889586 7.416198 266 TRUE Fold5
## 344 4.173207 4.969909 268 TRUE Fold5
## 345 6.884045 7.280110 269 TRUE Fold5
## 346 6.305209 6.496153 272 TRUE Fold5
## 347 6.784165 6.542171 273 TRUE Fold5
## 348 7.279933 7.099296 294 TRUE Fold5
## 349 6.412523 6.826419 298 TRUE Fold5
## 350 5.361428 5.594640 307 TRUE Fold5
## 351 7.138076 7.791020 309 TRUE Fold5
## 352 5.255064 4.571652 320 TRUE Fold5
## 353 5.299159 4.878524 328 TRUE Fold5
## 354 5.330002 5.522681 329 TRUE Fold5
## 355 7.475738 6.877500 334 TRUE Fold5
## 356 4.990159 5.594640 341 TRUE Fold5
## 357 6.316991 6.519202 344 TRUE Fold5
## 358 5.383585 5.648008 345 TRUE Fold5
## 359 6.884045 6.418723 353 TRUE Fold5
## 360 4.953175 5.300943 357 TRUE Fold5
## 361 7.293720 7.993748 361 TRUE Fold5model$resample## RMSE Rsquared MAE Resample
## 1 0.3502141 0.8112448 0.2619003 Fold1
## 2 0.4253605 0.7700303 0.3394721 Fold2
## 3 0.5631402 0.6023257 0.4515798 Fold3
## 4 0.4403479 0.7504798 0.3594414 Fold4
## 5 0.4790588 0.7710819 0.3900279 Fold5